Method for estimating position of ego vehicle for autonomous driving and autonomous driving apparatus

ABSTRACT

The present invention relates to a method for estimating a position of an ego vehicle for autonomous driving including the steps of: receiving first sensor inputs from first sensors to extract visual road information; allowing the extracted visual road information to match first map data to produce a first matching score group; receiving second sensor inputs from second sensors; allowing the received second sensor inputs to match the second map data to produce a second matching score group; checking whether the first matching score group is consistent to the second matching score group; and estimating any one of the position candidates in the position candidate group of the ego vehicle as the position of the ego vehicle according to the consistency checking result.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.62/624,978, filed Feb. 1, 2018, which is hereby incorporated byreference in its entirety into this application.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a method for estimating a position ofan ego vehicle for autonomous driving and an autonomous drivingapparatus, and more particularly, to a method for estimating a positionof an ego vehicle for autonomous driving and an autonomous drivingapparatus that are capable of estimating a position of an ego vehiclethrough first sensor inputs received from first sensors and secondsensor inputs received from second sensors.

2. Description of Related Art

Autonomous driving of a vehicle is a technology that is capable ofsensing the vehicle's surrounding environment and dangerous elements toset a driving path and further to control the vehicle's driving on thebasis of the set driving path, and the autonomous driving technology isdeveloped to minimize the intervention of a human driver.

An autonomous driving vehicle having a lane-assisting function is firstattempted, and recently, an autonomous driving vehicle having a fullautonomous driving function becomes commercialized. If an error occurson setting of an autonomous driving path, however, unintended accidentsmay happen frequently, and accordingly, it is considered that the fullautonomous driving function is not perfect. So as to solve suchproblems, many studies and developments have been made.

The autonomous driving path is set basically by receiving data fromsensors installed on a vehicle, analyzing the received data, andestimating the future position of the vehicle, and in conventionalpractices, accordingly, the autonomous driving path is set by receivingimages or image data from one kind of sensors, for example, camerasensors, and by analyzing the received images or data. If thecorresponding sensor does not work, however, there is no sensor capableof replacing the broken sensor, thereby making it impossible to set theautonomous driving path. If an error occurs on the data received fromthe sensor, further, it is possible to set the autonomous driving path,but the accuracy of the set autonomous driving path is remarkablydecreased, thereby undesirably raising possibility of an accident.

Therefore, there is a need for a technology capable of accuratelyestimating a future position of an ego vehicle to set an autonomousdriving path having no possibility of an accident and high reliability,even if a sensor does not work or an error occurs on data received fromthe sensor.

SUMMARY OF THE INVENTION

Accordingly, the present invention has been made in view of theabove-mentioned problems occurring in the prior art, and it is an objectof the present invention to provide a method for estimating a positionof an ego vehicle for autonomous driving and an autonomous drivingapparatus that are capable of accurately estimating a future position ofthe ego vehicle to set an autonomous driving path, thereby ensuringreliability of a full autonomous driving function.

The technical problems to be achieved through the present invention arenot limited as mentioned above, and other technical problems notmentioned herein will be obviously understood by one of ordinary skillin the art through the following description.

To accomplish the above-mentioned object, according to a first aspect ofthe present invention, there is provided a method for estimating aposition of an ego vehicle for autonomous driving, the method includingthe steps of: receiving first sensor inputs from first sensors toextract visual road information from the first sensor inputs by means ofan autonomous driving apparatus; allowing the extracted visual roadinformation to match first map data to produce a first matching scoregroup having first matching scores calculated with respect to positioncandidates in a position candidate group of the ego vehicle by means ofthe autonomous driving apparatus; receiving second sensor inputs fromsecond sensors as kinds of sensors different from the first sensors bymeans of the autonomous driving apparatus; allowing the received secondsensor inputs to match the second map data to produce a second matchingscore group having second matching scores calculated with respect toposition candidates in a position candidate group of the ego vehicle bymeans of the autonomous driving apparatus; checking whether the firstmatching score group is consistent to the second matching score group bymeans of the autonomous driving apparatus; and estimating any one of theposition candidates in the position candidate group of the ego vehicleas the position of the ego vehicle according to the consistency checkingresult by means of the autonomous driving apparatus.

According to the present invention, desirably, the first sensors arecamera sensors installed on the ego vehicle and the first map data ismap data having visual road information, such as lane markers, roadmarkers, curb and traffic sign.

According to the present invention, desirably, the second sensors arescanning sensors installed on the ego vehicle and the second map data is3D point cloud map data.

According to the present invention, desirably, the ego vehicle positionestimating method further includes, after the step of extracting thevisual road information, the steps of: checking whether the extractedvisual road information is effective; and checking whether the first mapdata is updated, and further includes, after the step of receiving thesecond sensor inputs, the steps of: checking whether the received secondsensor inputs are effective; and checking whether the second map data isupdated.

According to the present invention, desirably, the ego vehicle positionestimating method further includes the steps of: calculating firstmatching accuracy of the visual road information and the first map datathrough the effectiveness checking result of the visual road informationand the updating checking result of the first map data; and calculatingsecond matching accuracy of the second sensor inputs and the second mapdata through the effectiveness checking result of the second sensorinputs and the updating checking result of the second map data.

According to the present invention, desirably, the ego vehicle positionestimating method further includes the step of applying the firstmatching accuracy and the second matching accuracy as inputs of aweight-based probability coupling method to calculate a first weight forthe first matching accuracy and a second weight for the second matchingaccuracy.

According to the present invention, desirably, the step of estimatingany one of the position candidates as a position of the ego vehicleincludes the steps of: if the consistency checking result is “NO”,applying the first weight to the first matching scores calculated forthe position candidates in the position candidate group of the egovehicle and applying the second weight to the second matching scorescalculated for the position candidates in the position candidate groupof the ego vehicle to add the applied weights; and estimating theposition candidate having the biggest added result as the position ofthe ego vehicle.

According to the present invention, desirably, the step of checking theconsistency includes the steps of: calculating a difference between aprobability distribution of the first matching score group and aprobability distribution of the second matching score group by means ofa probability distribution comparison method; and determining whetherthe calculated difference is over a given threshold value.

According to the present invention, desirably, the step of estimatingany one of the position candidates as a position of the ego vehicleincludes the steps of: if the consistency checking result is “YES”,adding the first matching scores and the second matching scorescalculated for the position candidates in the position candidate groupof the ego vehicle; and estimating the position candidate having thebiggest added result as the position of the ego vehicle.

To accomplish the above-mentioned object, according to a second aspectof the present invention, there is provided an autonomous drivingapparatus for estimating a position of an ego vehicle, including: one ormore processors; a network interface; a memory for loading a computerprogram implemented by the processors; and a storage for storinglarge-capacity network data and the computer program, wherein thecomputer program performs: an operation for receiving first sensorinputs from first sensors to extract visual road information from thefirst sensor inputs received; an operation for allowing the extractedvisual road information to match first map data to produce a firstmatching score group having first matching scores calculated withrespect to position candidates in a position candidate group of the egovehicle; an operation for receiving second sensor inputs from secondsensors as the kinds of sensors different from the first sensors; anoperation for allowing the received second sensor inputs to match secondmap data to produce a second matching score group having second matchingscores calculated with respect to position candidates in a positioncandidate group of the ego vehicle; an operation for checkingconsistency between the first matching score group and the secondmatching score group; and an operation for estimating any one of theposition candidates in the position candidate group of the ego vehicleas the position of the ego vehicle according to the consistency checkingresult.

To accomplish the above-mentioned object, according to a third aspect ofthe present invention, there is provided a computer program stored in amedium in such a manner as to be coupled to a computing device to allowthe computing device to execute the steps of: receiving first sensorinputs from first sensors to extract visual road information from thefirst sensor inputs received; allowing the extracted visual roadinformation to match first map data to produce a first matching scoregroup having first matching scores calculated with respect to positioncandidates in a position candidate group of an ego vehicle; receivingsecond sensor inputs from second sensors as the kinds of sensorsdifferent from the first sensors; allowing the received second sensorinputs to match second map data to produce a second matching score grouphaving second matching scores calculated with respect to positioncandidates in a position candidate group of the ego vehicle; checkingconsistency between the first matching score group and the secondmatching score group; and estimating any one of the position candidatesin the position candidate group of the ego vehicle as a position of theego vehicle according to the consistency checking result.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentinvention will be apparent from the following detailed description ofthe preferred embodiments of the invention in conjunction with theaccompanying drawings, in which:

FIG. 1 is a flow chart showing representative steps in a method forestimating a position of an ego vehicle for autonomous driving accordingto a first embodiment of the present invention;

FIG. 2 is a photograph showing examples of first sensor inputs accordingto the present invention;

FIG. 3 shows lane markers, road markers, curb and traffic sign extractedfrom the first sensor input of FIG. 2;

FIG. 4 shows an example of a road network map as first map data;

FIG. 5 shows an example of a local road network map on which the lanemarkers, road markers, curb and traffic sign extracted from the firstsensor input match the first map data;

FIG. 6 shows an example of the lane markers, road markers, curb andtraffic sign on a road along which the ego vehicle is currently drivento explain a method for producing a first matching score group;

FIG. 7 is a perspective view showing an example of a LiDAR sensormounted on the ego vehicle;

FIG. 8 shows an example of second sensor inputs according to the presentinvention;

FIG. 9 shows an example of a 3D point cloud as second map data inclusiveof grids;

FIG. 10 shows an example of a local point cloud map on which the secondsensor inputs match the second map data;

FIG. 11 is a flow chart showing detailed steps in a step S150 accordingto the present invention;

FIG. 12 visually shows the steps of calculating a difference between aprobability distribution of a first matching score group and aprobability distribution of a second matching score group by means ofthe Kullback-Leibler divergence;

FIG. 13 is a flow chart showing representative steps in a method forestimating a position of an ego vehicle for autonomous driving accordingto a second embodiment of the present invention; and

FIG. 14 is a block diagram showing a configuration of an autonomousdriving apparatus for estimating a position of an ego vehicle accordingto a third embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, the present invention is disclosed with reference to theattached drawings. Objects, characteristics and advantages of thepresent invention will be more clearly understood from the detaileddescription as will be described below and the attached drawings. Beforethe present invention is disclosed and described, it is to be understoodthat the disclosed embodiments are merely exemplary of the invention,which can be embodied in various forms. Therefore, specific structuraland functional details disclosed herein are not to be interpreted aslimiting, but merely as a basis for the claims and as a representativebasis for teaching one of ordinary skill in the art to variously employthe present invention in virtually any appropriately detailed structure.In the description, the corresponding parts in the embodiments of thepresent invention are indicated by corresponding reference numerals.

All terms used herein, including technical or scientific terms, unlessotherwise defined, have the same meanings which are typically understoodby those having ordinary skill in the art.

The terms, such as ones defined in common dictionaries, should beinterpreted as having the same meanings as terms in the context ofpertinent technology, and should not be interpreted as having ideal orexcessively formal meanings unless clearly defined in the specification.An expression referencing a singular value additionally refers to acorresponding expression of the plural number, unless explicitly limitedotherwise by the context.

In this application, terms, such as “comprise”, “include”, or ‘have”,are intended to designate those characteristics, numbers, steps,operations, elements, or parts which are described in the specification,or any combination of them that exist, and it should be understood thatthey do not preclude the possibility of the existence or possibleaddition of one or more additional characteristics, numbers, steps,operations, elements, or parts, or combinations thereof.

Hereinafter, the present invention is in detail disclosed with referenceto the attached drawings.

FIG. 1 is a flow chart showing representative steps in a method forestimating a position of an ego vehicle for autonomous driving accordingto a first embodiment of the present invention.

The flow chart as shown in FIG. 1 is desirable in accomplishing theobjects of the present invention, but if necessary, of course, somesteps may be added or removed.

On the other hand, the method for estimating a position of an egovehicle for autonomous driving according to a first embodiment of thepresent invention as will be discussed later is carried out by means ofan autonomous driving apparatus 100. The autonomous driving apparatus100 is an independent apparatus which is mounted on a vehicle, andotherwise, it is built in a control device of the vehicle, as a form ofa computer program stored in a medium, which will be discussed later.

Moreover, a term “ego vehicle” means a user or driver's own vehicledriven by him or her, which has the same meaning as a vehicle which hasto set an autonomous driving path.

In step S110, first, the autonomous driving apparatus 100 receives firstsensor inputs from first sensors 2 to extract visual road informationsuch as lane markers, road markers, curb and traffic sign from the firstsensor inputs.

In this case, the first sensors 2 are camera sensors installed on theego vehicle 5. For example, the camera sensors are one or more sensorsselected from a front sensing camera sensor installed on the front sideof the ego vehicle 5, camera sensors for around view installed on sidemirrors of the ego vehicle 5, and a rear sensing camera sensor installedon the rear side of the ego vehicle 5. If the first sensors 2 are anycamera sensors installed on the ego vehicle 5, they do not matter.

On the other hand, since the first sensors 2 are camera sensorsinstalled on the ego vehicle 5, the first sensor inputs received fromthe first sensors 2 are images or image data for the front, side, rear,and surrounding environment of the ego vehicle 5.

The autonomous driving apparatus 100 extracts visual road informationsuch as lane markers, road markers, curb and traffic sign from the firstsensor inputs. FIG. 2 is a photograph showing examples of the firstsensor inputs, and FIG. 3 shows visual road information, such as lanemarkers, road markers and traffic sign extracted from the first sensorinputs of FIG. 2.

In this case, the lane markers include all kinds of lane markers, suchas, a solid line lane, a dashed line lane, a solid and dashed line lane,and a double line lane, and also, they include all colors of lanes, suchas, a yellow lane, a white lane, a blue lane, and a road boundary likecurb.

On the other hand, the road markers includes a left turn marker, a rightturn marker, a straight and left turn marker, a straight and right turnmarker, a prohibited straight marker, a prohibited left turn marker, aprohibited right turn marker, a stop marker, a U turn marker, a speedlimit marker, a crosswalk marker on the road and further includesmarkers related to paths indicated on traffic signs.

Furthermore, the visual road information includes road information, suchas, a stop line, a crossway, a curb boundary, and in the description,the lane markers, road markers and traffic sign as examples of thevisual road information will be explained.

The visual road information including lane markers, road markers, curband traffic sign are extracted by means of image processing, and forexample, they are extracted from the first sensor inputs by means of alane marker detection algorithm, a road marker, curb detection, ortraffic sign detection algorithm using machine learning, deep learning,and a feature extraction or image filtering algorithm.

If the visual road information such as lane markers and road markers—areextracted from the first sensor inputs, in step S120, they match firstmap data by means of the autonomous driving apparatus 100 to produce afirst matching score group having first matching scores calculated withrespect to position candidates in a position candidate group of the egovehicle.

In this case, the first map data is map data having the visual roadinformation such as lane markers, road markers, curbs and traffic sign—,which is called road network map. FIG. 4 shows an example of the roadnetwork map as the first map data, and FIG. 5 shows an example whereinthe extracted lane markers, road markers, curbs and traffic sign matchthe road network map, which is called local road network map.

On the other hand, the position candidate group of the ego vehicle 5 isa group from a current position to estimable positions to be locatedafter moving for a given period of time, which is produced by means ofthe results obtained by allowing visual road information such as theextracted lane markers and road markers to match the first map data. Forexample, if the visual road information such as extracted lane markers,road markers, curb and traffic sign—are a left turn marker, a right turnmarker, and a straight marker, the position candidate group of the egovehicle 5 includes a position a after a left turn, a position b after aright turn, and a position c after straight, and the autonomous drivingapparatus 100 calculates the first matching scores for the respectiveposition candidates.

In this case, the calculation of the first matching scores is carriedout, while focusing on the visual road information such as lane markers,road markers, curb and traffic sign—on a road along which the egovehicle 5 is currently driven, and FIG. 6 shows an example of the lanemarkers and road markers on a road along which the ego vehicle iscurrently driven.

Referring to FIG. 6, it can be checked that the ego vehicle 5 is drivenon a third lane on a four-lane road, a left turn marker exists on afirst lane, a straight marker on a second lane, a straight marker on athird lane, and a right turn marker on a fourth lane. In this case, theego vehicle 5 is driven on the third lane on the four-lane road, and ifit is assumed that the straight marker in front of the ego vehicle 5 andthe right turn mark on the fourth lane are recognized by means of thefirst sensors 2 and the straight marker on the second lane is notrecognized, the position candidate group includes the second lane, thethird lane, and the fourth lane.

First, the position candidate a assumed as the second lane positioncorresponds to the straight marker on the first map data, thereby havinga good first matching score, but since even the right turn marker on thefourth lane is recognized by means of the first sensors 2, the secondlane position matches the straight marker on the third lane, so that thefirst matching score of the position candidate a becomes low.Contrarily, the position candidate b assumed as the third lane positioncorresponds to the straight marker and the right turn marker at theright side thereof on the first map data, thereby having a high firstmatching score. The position candidate c assumed as the fourth laneposition recognizes the straight marker in front of the ego vehicle 5,even though the right turn marker exists, thereby having a low firstmatching score, and further, it recognizes the right turn marker on theright lane thereof. Since the position candidate c is the positioncandidate located on the fourth lane of the four-lane road, however, thefirst matching score of the position candidate c becomes much lower thanbefore.

In detail, the first matching scores are calculated in the rank order ofthe first match score of the position candidate b assumed as the thirdlane position, the first match score of the position candidate a assumedas the second lane position, and the first match score of the positioncandidate c assumed as the fourth lane position. Accordingly, theposition candidate b assumed as the third lane position has the highestfirst matching score.

As mentioned above, the method for producing the first matching scoregroup explained with reference to FIG. 6 is just exemplary, and ofcourse, the first matching score group may be produced through othermethods. In all cases, however, the first matching score group isproduced by means of the local road network map as the results obtainedby allowing the visual road information including extracted lanemarkers, road markers, curb and traffic sign—to match the first mapdata.

Referring back to FIG. 1, the method for estimating the position of theego vehicle will be explained.

If the first matching score group is produced, the autonomous drivingapparatus 100 receives second sensor inputs from second sensors 4 askinds of sensors different from the first sensors 2 in step S130.

In this case, the second sensors 4 are scanning sensors installed on theego vehicle 5, in more detail, LiDAR sensors. FIG. 7 is a perspectiveview showing an example of the LiDAR sensor mounted on the ego vehicle,and the LiDAR sensor is installed on a roof of the ego vehicle 5 tosense the ego vehicle 5 and the surrounding environment around the egovehicle 5, in more detail, 360° view around the ego vehicle 5.

On the other hand, the second sensors 4 may be installed on anotherportion of the ego vehicle 5, and in this case, it should be installedat a relatively distant position from the ground so as to sense the egovehicle 5 and the environment around the ego vehicle 5. For example, thesecond sensors 4 may be installed on a bonnet or trunk of the egovehicle 5.

The second sensor inputs of the autonomous driving apparatus 100received from the second sensors 4 are raw data, and FIG. 8 showsexamples of the second sensor inputs according to the present invention.Referring to FIG. 8, the second sensor inputs include the ego vehicle 5having the second sensors 4 installed on the center thereof, which isnot omitted in the figure, and surrounding situations as objectsexisting around the ego vehicle 5 within a given distance.

On the other hand, the second sensor inputs as shown in FIG. 8 areprovided with a top view for the ego vehicle 5 and the situations aroundthe ego vehicle 5, but they are just exemplary. In some cases, ofcourse, the second sensor inputs may be provided with a side orperspective view.

If the second sensor inputs are received, the autonomous drivingapparatus 100 allows the second sensor inputs to match the second mapdata to produce a second matching score group having second matchingscores calculated with respect to position candidates in a positioncandidate group of the ego vehicle in step S140.

In this case, the second map data is data on a map retained by the egovehicle 5. The second map data is not limited in form, but so as torapidly compare the second map data with the second sensor inputsreceived in real time, desirably, the second map data has the datahaving the same form as the second sensor inputs or having the similarform thereto. For example, the second map data may be map data producedby accumulating the second sensor inputs received from the secondsensors 4 for a relatively long period of time.

On the other hand, the second map data is not limited in form, butdesirably, it has grids. In this case, the grids include information onthe mean of a dispersion of a height distribution of all points existingtherein.

Furthermore, the grids include any one or more information selected frominformation on heights of all points existing therein, information on aheight distribution, information on a dispersion of the heightdistribution, and information on the mean of the dispersion of heightdistribution. Further, the grids include information on the mean of adispersion of a height distribution of continuous points among allpoints existing therein and a plurality of information as mentionedabove. In case of a road close to a tree, for example, the ground existstogether with the branch of tree extended laterally, and in this case,the mean of a dispersion of a height distribution of the ground and themean of a dispersion of a height distribution of the branch areindividually processed and included in the grids.

FIG. 9 shows an example of the second map data having the grids. Onegrid is a small size of 0.2 m*0.2 m, and accordingly, the grids are noteasily distinguished. However, each point as shown in FIG. 9 includesthe means of a dispersion of a height distribution of one grid and theadditional information as mentioned above, which is a 3D point cloud.FIG. 10 shows an example on which the second sensor inputs match the 3Dpoint cloud as the second map data, which is called LiDAR-based heightmap matching. Upon the map matching, an area used really on the wholeLiDAR height map is an area around the position candidate group, and soas to separately define an interesting area of the map, the LiDAR heightmap is also called a local map or local point cloud map.

In the same manner as in the method for producing the first matchingscore group, on the other hand, the position candidate group of the egovehicle 5 is a group from a current position to estimable positions tobe located after moving for a given period of time, but unlike the firstmatching score group, the second sensor inputs and the second map datainclude the information on the lane markers and road markers as well asthe information on the surrounding situations like the objects existingaround the ego vehicle 5. According to the features of the presentinvention, above all, the second matching score group is produced withthe use of static objects such as curb, building, forest, bus stop,traffic light, and so on, which are different from the lane markers,road markers, curb and traffic sign used for the production of the firstmatching score group, through the second sensors 4 different from thefirst sensors 2, thereby desirably improving accuracy in the estimationof the position of the ego vehicle 5.

Accordingly, the second matching score group is produced by means of thegrids in the second map data. In more detail, the grids include theinformation on the mean of the dispersion of height distribution of allpoints existing therein, the information on heights, the information onheight distribution, the information on dispersion of the heightdistribution, the information on the mean of the height distribution,and the information on the mean of dispersion of height distribution ofcontinuous points existing therein, and with the use of any one or moreinformation among the information, second matching scores for therespective position candidates in the position candidate group of theego vehicle 5 can be calculated.

In this case, the calculation of the second matching scores has to becarried out for the same position candidate group as the positioncandidate group of the ego vehicle 5 for which the first matching scoresare calculated, and accordingly, it is checked whether the secondmatching score group is consistent to the first matching score group, instep S150 as will be discussed later. In this case, if theabove-mentioned examples are adopted, the second matching scores have tobe calculated for the position candidate a assumed as the second laneposition, the position candidate b assumed as the third lane position,and the position candidate c assumed as the fourth lane position.

In the calculation of the second matching scores, first, information onthe grids matching the current position of the ego vehicle 5 is read,and next, the read grid information is compared with grid informationmatching the position candidate a assumed as the second lane position,grid information matching the position candidate b assumed as the thirdlane position, and grid information matching the position candidate cassumed as the fourth lane position. Accordingly, a high second matchingscore is given to the position candidates of the ego vehicle 5, whichhave a small information difference. As mentioned above, the gridsinclude the information on the mean of the dispersion of heightdistribution of all points existing therein, the information on heights,the information on height distribution, the information on dispersion ofthe height distribution, the information on the mean of the heightdistribution, and the information on the mean of dispersion of heightdistribution of continuous points existing therein, and if a differencebetween the grid information is small, accordingly, the positioncandidate may be the same road connected to the road on which the egovehicle 5 is current located. Contrarily, if a difference between thegrid information is big, accordingly, the position candidate may be notthe road on which the ego vehicle 5 is current located, but an object,such as a building, tree, or the like.

As mentioned above, the method for producing the second matching scoregroup is just exemplary, and of course, the second matching score groupmay be produced through other methods. In all cases, however, the secondmatching score group is produced by means of the local height map as theresults obtained by allowing the second sensor inputs to match thesecond map data.

Referring back to FIG. 1, the method for estimating the position of theego vehicle will be explained.

If the second matching score group is produced, it is checked whetherthe first matching score group is consistent to the second matchingscore group by means of the autonomous driving apparatus 100 in stepS150.

In more detail, as shown in FIG. 11, the step S150 includes the stepsof: calculating a difference between a probability distribution of thefirst matching score group and a probability distribution of the secondmatching score group by means of a probability distribution comparisonmethod in step S150-1; determining whether the calculated difference isover a given threshold value in step S150-2; if the calculateddifference is over the given threshold value, determining that the twoprobability distributions are not consistent to each other in stepS150-3; and if the calculated difference is under the given thresholdvalue, determining that the two probability distributions are consistentto each other in step S150-4. In this case, the probability distributioncomparison method is freely adopted, and for example, theKullback-Leibler divergence (KLD) and Levene's test for equality ofvariances are representative. Otherwise, differences between the meanand variances of the two probability distributions may be directlycompared with each other. Hereinafter, the Kullback-Leibler divergenceas the probability distribution comparison method will be explained.

In this case, the given threshold value is a value set in advance, withwhich the difference between the probability distribution of the firstmatching score group and the probability distribution of the secondmatching score group calculated by means of the Kullback-Leiblerdivergence is compared to determine whether the two probabilitydistributions are consistent or not. The given threshold value can beset as a free value by a designer of the autonomous driving apparatus100. In this case, if the given threshold value is set small, results ofdetermining that the two probability distributions are not consistent toeach other are increased, which makes it possible to accurately estimatethe position of the ego vehicle 5.

FIG. 12 shows the step of determining whether the difference between theprobability distribution of the first matching score group and theprobability distribution of the second matching score group calculatedby means of the Kullback-Leibler divergence is over the given thresholdvalue, in more detail, whether the two probability distributions areconsistent or not. A graph in a shape of grids is the probabilitydistribution of the first matching score group for the positioncandidate group of the ego vehicle 5 calculated with the first sensorinputs, and a graph in a shape of a sharp polygon is the probabilitydistribution of the second matching score group for the positioncandidate group of the ego vehicle 5 calculated with the second sensorinputs. For the conveniences of the calculation, moreover, the twoprobability distributions are displayed with the grids, but they are notlimited necessarily thereto. Even if random position candidate groupsare provided, they are approximated to a multivariate probabilitydistribution on the basis of the value, thereby calculating therespective probability distributions.

Referring to (a) of FIG. 12, in this case, a high point of theprobability distribution of the first matching score group is consistentto a high point of the probability distribution of the second matchingscore group, and Referring to (b) of FIG. 12, contrarily, a high pointof the probability distribution of the first matching score group islocated at a left side from a high point of the probability distributionof the second matching score group. Accordingly, (a) of FIG. 12 showsthat a difference between the two probability distributions is under thegiven threshold value, so that it is determined that the two probabilitydistributions are consistent to each other, and contrarily, (b) of FIG.12 shows that a difference between the two probability distributions isover the given threshold value, so that it is determined that the twoprobability distributions are not consistent to each other.

As mentioned above, the method for checking the consistency is justexemplary, and of course, the consistency may be checked through othermethods. In all cases, however, the consistency is checked by means ofthe first matching score group and the second matching score groupproduced for the same position candidate group of the ego vehicle 5 aseach other.

If the consistency is checked, the autonomous driving apparatus 100estimates any one of the position candidates in the position candidategroup of the ego vehicle 5 as the position of the ego vehicle 5according to the consistency checking result in step S160.

As mentioned above, if the calculated difference is over the giventhreshold value, it is determined that the two probability distributionsare not consistent to each other in the step S150-3, and if thecalculated difference is under the given threshold value, it isdetermined that the two probability distributions are consistent to eachother in the step S150-4. Referring to FIG. 11, the step S160 furtherincludes the steps of: if the consistency checking result is “YES”,adding the first matching scores and the second matching scorescalculated for the position candidates in the position candidate groupof the ego vehicle 5 in step S160-1; and estimating the positioncandidate having the biggest added result as the position of the egovehicle 5 in step S160-2.

In this case, the step S160 will be explained in detail with theabove-mentioned example. If the consistency checking result is “YES”,the first matching scores and the second matching scores, which arecalculated for the position candidate a assumed as the second laneposition, the position candidate b assumed as the third lane position,and the position candidate c assumed as the fourth lane position as theposition candidates in the position candidate group of the ego vehicle5, are added, and the position candidate having the biggest added resultis estimated as the position of the ego vehicle 5. In this case, thematching scores are calculated from the data received through the twokinds of sensors, not one sensor, and further, as the consistency ischecked, the position candidate having the biggest added result can beestimated as the position of the ego vehicle 5.

If the consistency checking result is “NO”, on the other hand, theposition of the ego vehicle 5 is estimated through separate correction,which will be explained later in steps S160-3 and S160-4.

So far, the method for estimating the position of the ego vehicleaccording to the first embodiment of the present invention has beenexplained. According to the present invention, data is received from thedifferent kinds of sensors, unlike the conventional autonomous drivingapparatus, and after the analysis of the data, the consistency ischecked to estimate the position of the ego vehicle 5. Accordingly, evenif any one of the sensors does not work or there is an error on the datareceived from any one of the sensors, it is possible to accurately setthe autonomous driving path, thereby enabling the autonomous drivingapparatus 100 to have high reliability.

Hereinafter, an explanation on the case wherein the consistency checkingresult is “NO” and additional technical features for improving theaccuracy in the estimation of the position of the ego vehicle 5 will begiven.

FIG. 13 is a flow chart showing representative steps in a method forestimating a position of an ego vehicle for autonomous driving accordingto a second embodiment of the present invention.

The flow chart as shown in FIG. 13 is desirable in accomplishing theobjects of the present invention, but if necessary, of course, somesteps may be added to or removed.

When compared with the method for estimating the position of the egovehicle for autonomous driving according to the first embodiment of thepresent invention, the method for estimating the position of the egovehicle for autonomous driving according to the second embodiment of thepresent invention further includes the data effectiveness checking step,the data updating checking step, and the weight calculating and applyingstep.

After the step S110 in which the visual road information such as lanemarkers, road markers, curb and traffic sign are extracted from thefirst sensor inputs, first, it is checked whether the visual roadinformation including extracted lane markers, road markers, curb andtraffic sign—are effective in step S112, and next, it is checked whetherthe first map data is updated in step S114. After the step S130 in whichthe second sensor inputs are received, it is checked whether thereceived second sensor inputs are effective in step S132, and next, itis checked whether the second map data is updated in step S134.

In this case, the steps S112 and S132 of checking the effectiveness arecarried out by checking whether the visual road information such asextracted lane markers and road markers match the first map data andwhether the second sensor inputs match the second map data, andtherefore, the steps S112 and S132 are called sensor quantification.

In this case, if the sensor quantification for the first sensors isindicated by a mathematical expression, k_(RN)‘ (x_(c))=exp(−λ_(RN)Σ_(i∈lanes)R(X_(l,Suff) ^(i)−X_(l) ^(i))), and if the sensorquantification for the second sensors is indicated by a mathematicalexpression, k_(H)(X_(H))=exp(−λ_(H)R(X_(H,Suff)−x_(H))), wherein each ofthe x_(l) ^(i) and X_(H,Suff) is a threshold point for the number ofoptimal features needed for extracting accurate position information,each of the x_(H) and x_(l) ^(i) is the number of features currentlyreceived on the sensors, and the R(x) determines whether the number offeatures of the sensors for matching is sufficient or not, which iscarried out by means of a sigmoid function or rectified unit.

On the other hand, in the steps S114 and S134 of checking the updating,since the first map data and the second map data are map data, theobjects like buildings, roads and so on may be changed by newconstruction or removal, and accordingly, it is checked whether thefirst map data and the second map data are updated to reflect thecurrent states of the objects well. The steps S114 and S134 are calledmap quantification. The local map for the current position of the egovehicle 5 is retrieved from the whole map data, and it is then checkedwhether the local map has richful information.

In this case, if the map quantification for the first map data isindicated by a mathematical expression, g_(RN)‘ (l_(c),t)=exp(−λ_(s)R(l_(C,Suff)−l_(C)))P_(idx)(t), and if the mapquantification for the second map is indicated by a mathematicalexpression,g_(L)‘(C_(L),t)=exp(−λ_(S)R(C_(L,Suff)−C_(L)))P_(up2date)(t), whereinthe R(x) determines whether the richness of the local map informationfor matching is over a given level, which has the almost same concept asthe sensor quantification. The richness of the local map informationmeans the richness of information within the radius of tens of metersfrom every position of the ego vehicle 5. Accordingly, the road networkmap as the first map data has sufficient information on the lanemarkers, road markers, curb and traffic sign within a given range, andthe 3D point cloud as the second map data has information on variousheights. Further, the P_(up2date)(t) checks whether the map is updated,which is needed because the reliability of the map serving as groundtruth in a map matching technique may be deteriorated due to changes ofsurrounding environments as time passes. In this case, since the secondsensor-based second map data expressing the detailed 3D map informationis relatively weak for time change, the P_(up2date)(t) of the 3D pointcloud map has a relatively more drastic decrement in reliability for thesame time change than the road network map, which is thus approximatedon the basis of the experimental verification.

If the updating is checked, first matching accuracy of the lane markers,road markers, curb and traffic sign and the first map data iscalculated, in step

S116, through the effectiveness checking result of the visual roadinformation such as the lane markers and road markers and the updatingchecking result of the first map data, and second matching accuracy ofthe second sensor inputs and the second map data is calculated, in stepS136, through the effectiveness checking result of the second sensorinputs and the updating checking result of the second map data.

In this case, the first matching accuracy is calculated through thesensor quantification of the first sensors as the effectiveness checkingresult of visual road information such as lane markers, road markers,curb and traffic sign-and the map quantification as the updatingchecking result of the first map data, and the second matching accuracyis calculated through the sensor quantification of the second sensors asthe effectiveness checking result of the second sensor inputs and themap quantification as the updating checking result of the second mapdata. At this time, the respective checking results are quantified.

For example, the quantified first and second matching accuracy may be afunction f(x) and mediating variables alpha and beta applied as inputsof the Dempster Shafer theory as will be discussed later or may benumbers or scores within a given range. In any case, they have to havethe form of the inputs applied to the Dempster Shafer theory as will bediscussed later to calculate weights.

If the first matching accuracy and the second matching accuracy arecalculated, they are applied as the inputs of the Dempster Shafer theoryas one of weight-based probability coupling methods to calculate a firstweight for the first matching accuracy and a second weight for thesecond matching accuracy in step S155.

The Dempster Shafer theory is a known theory used for uncertaintyhandling, and if the Dempster Shafer theory is adopted in the presentinvention, it can be indicated by the following mathematicalexpressions.

⁢m S ⁡ ( A ) = { m S ⁡ ( { H } ) = k H ′ ⁡ ( x H ) m S ⁡ ( { L } ) = k RN ′ ⁡( x C ) m S ⁡ ( { H , L } ) = Θ S ⁢ ⁢ ⁢ m M ⁡ ( A ) = { m M ⁡ ( { H } ) = g L′ ⁡ ( C L ) m M ⁡ ( { L } ) = g RN ′ ⁡ ( l C ) m M ⁡ ( { H , L } ) = Θ M ⁢ ⁢ ⁢∑ A ⋐ X ⁢ m ⁡ ( A ) = 1

In this case, the m_(s)(A) is the information on the matchingreliability in the matching of the first sensors 2 and the matching ofthe second sensors 4 when seen in view of the sensors, which applies thesensor reliability value k(x) calculated in the sensor quantification toeach math case m_(s)(L), m_(s)(H).

In this case, the mM(A) is the information on the matching reliabilityin the matching of the first sensors 2 and the matching of the secondsensors 4 when seen in view of the maps, which applies the sensorreliability value k(x) calculated in the map quantification to each mathcase m_(M)(H), m_(M)(L),

The m_(s)({H, L}) is a value like Θ for a situation in which it isdifficult to check the reliability in matching of the two sensors, andthe sum of m_(s)({H}), m_(s)({L}), m_(s)({H, L}) is normalized to be setto 1 by means of the Dempster Shafer theory.

The m_(s,M)(A) is a result of totally considering the two math m_(s)(A),mM(A) and the sum of the two math is calculated by the Dempster Shafertheory as follows:

$\mspace{135mu}{{m_{S,M}(H)} = {{\left( {m_{S} \oplus m_{M}} \right)(H)} = \frac{\sum\limits_{{B\bigcap C} = H}{{m_{S}(B)}{m_{M}(C)}}}{1 - K}}}$$\mspace{140mu}{{m_{S,M}(L)} = {{\left( {m_{S} \oplus m_{M}} \right)(L)} = \frac{\sum\limits_{{B\bigcap C} = L}{{m_{S}(B)}{m_{M}(C)}}}{1 - K}}}$${K = {\sum\limits_{{B\bigcap C} = \Phi}{{m_{S}(B)}{m_{M}(C)}}}}$

The m_(s)({L}) is reliability in the matching of the second sensors 4under total consideration for the sensor and map features, and them_(S,M)({L}) is reliability in the matching of the first sensors 2 undertotal consideration for the sensor and map features. The weight r is avalue obtained by normalizing the two values.

$r = \frac{m_{S,M}\left( \left\{ H \right\} \right)}{{m_{S,M}\left( \left\{ L \right\} \right)} + {m_{S,M}\left( \left\{ H \right\} \right)}}$

On the other hand, the first weight and the second weight are expressedwith the term ‘weight’, but measurements as the map matching results forthe position candidates may become the weights. Since the first sensors2 and the second sensors 4 different from the first sensors 2 are used,the two measurements exist, and also, reliability weights used for thebalancing of the two measurements may become the weights. Thereliability weights can be considered as reliability of matchingsources.

${\hat{x}}_{k} = {\underset{\hat{x_{k}^{l}}}{{\arg\;\max}\;}{p\left( {\left. x_{k}^{i} \middle| z_{k}^{RN} \right.,m_{RN}} \right)}^{1 - r}{p\left( {\left. x_{k}^{i} \middle| z_{k}^{H} \right.,m_{H}} \right)}^{r}}$

The above mathematical expression is a mathematical expression relatedto pose estimation used to estimate the position of the ego vehicle 5,wherein the x_(k) ^(i)|z_(k) ^(RN),m_(RN) is the weight for the firstsensors 2, the X_(k) ^(i)|Z_(k) ^(H),m_(H) is the weight for the secondsensors 4, and the r is the reliability weight. Like this, the weightshave ambiguous expressions, but it is understood that the weights aredistinguishedly used in estimating the position of the ego vehicle 5.

After the first weight and the second weight are calculated, the step ofestimating the position of the ego vehicle 5 through the separatecorrection is carried out if the consistency checking result is “NO” inthe step S160 as mentioned above.

In more detail, if the consistency checking result is “NO”, as shown inFIG. 11, the step S160 of estimating any one of the position candidatesas the position of the ego vehicle 5 includes: the step S160-3 ofapplying the first weight to the first matching scores for the positioncandidates in the position candidate group of the ego vehicle 5 andapplying the second weight to the second matching scores for theposition candidates in the position candidate group of the ego vehicle 5to add the applied weights; and the step S160-4 of estimating theposition candidate having the biggest added result as the position ofthe ego vehicle 5.

In this case, the application of the first weight to the first matchingscores and the application of the second weight to the second matchingscores are carried out by means of operations like multiplication, andthe step S160-4 is the same as the step S160-2, and for the brevity ofthe description, a detailed explanation on the step S160-4 will beavoided.

So far, the method for estimating the position of the ego vehicle forautonomous driving according to the second embodiment of the presentinvention has been explained. According to the present invention, theeffectiveness of the lane markers, road markers, curb and traffic signextracted from the first sensor inputs and the effectiveness of thesecond sensor inputs are checked, and the updating of the first map dataand the second map data is checked, so that with the use of the checkingresults, the first matching scores and the second matching scores arecorrected, thereby more improving the accuracy in the estimation of theposition of the ego vehicle 5, and even if the consistency checkingresult is “NO”, it is possible to more accurately estimate the positionof the ego vehicle 5.

On the other hand, the methods for estimating the position of the egovehicle for autonomous driving according to the first and secondembodiments of the present invention are carried out by means of theautonomous driving apparatus 100 for estimating the position of the egovehicle 5 according to a third embodiment of the present invention, andthe autonomous driving apparatus 100 has the same technical features asthe methods for estimating the position of the ego vehicle. In thiscase, as shown in FIG. 14, the autonomous driving apparatus 100 forestimating the position of the ego vehicle 5 according to the thirdembodiment of the present invention includes a processor 10, a networkinterface 20, a memory 30, a storage 40, and a data bus 50 forconnecting them.

The processor 10 serves to control the whole operations of therespective parts. The processor 10 is any one of a CPU (CentralProcessing Unit), MPU (Micro Processor Unit), MCU (Micro ControllerUnit), and a processor as widely known to the technical field related tothe present invention. Moreover, the processor 10 performs operationsfor at least one application or program which implements the methods forestimating the position of the ego vehicle for autonomous drivingaccording to the first and second embodiments of the present invention.

The network interface 20 supports wired and wireless internetcommunication of the autonomous driving apparatus 100 for estimating theposition of the ego vehicle 5 according to the third embodiment of thepresent invention and further supports other known communication.Accordingly, the network interface 20 may have a communication modulefor the corresponding communication.

The memory 30 stores all kinds of data, command and/or information andloads one or more computer programs 41 from the storage 40 thereinto soas to perform the methods for estimating the position of the ego vehiclefor autonomous driving according to the first and second embodiments ofthe present invention. In FIG. 14, a RAM as the memory 30 is shown, butof course, various storage media may be used as the memory 30.

The storage 40 non-temporarily stores one or more computer programs 41and large-capacity network data. The storage 40 is any one of anon-volatile memory, such as ROM (Read Only Memory), EPROM (ErasableProgrammable ROM), EEPROM (Electrically Erasable Programmable ROM), andflash memory, a hard disk, a detachable disk, and a recording mediumreadable by a computer, which is widely known to the technical fieldrelated to the present invention.

The computer program 41 is loaded to the memory 30 to perform anoperation for receiving first sensor inputs from first sensors by meansof the processor 10 to extract visual road information such as lanemarkers, road markers, curb and traffic sign from the received firstsensor inputs, an operation for allowing the extracted visual roadinformation such as lane markers, road markers, curb and traffic sign tomatch first map data to produce a first matching score group havingfirst matching scores calculated with respect to position candidates ina position candidate group of the ego vehicle, an operation forreceiving second sensor inputs from second sensors as the kind of sensordifferent from the first sensors, an operation for allowing the receivedsecond sensor inputs to match second map data to produce a secondmatching score group having second matching scores calculated withrespect to position candidates in a position candidate group of the egovehicle, an operation for checking the consistency between the firstmatching score group and the second matching score group, and anoperation for estimating any one of the position candidates in theposition candidate group of the ego vehicle as the position of the egovehicle according to the consistency checking result.

On the other hand, the methods for estimating the position of the egovehicle for autonomous driving according to the first and secondembodiments of the present invention are carried out by means of acomputer program stored in a medium according to a fourth embodiment ofthe present invention, and the computer program has the same technicalfeatures as the methods for estimating the position of the ego vehicle.Even though not described in detail for the brevity of the description,all technical features of the methods for estimating the position of theego vehicle for autonomous driving according to the first and secondembodiments of the present invention are applied to both of the computerprogram stored in a medium according to the fourth embodiment of thepresent invention and the autonomous driving apparatus 100 forestimating the position of the ego vehicle 5 according to the thirdembodiment of the present invention, thereby obtaining the same effectsas each other.

As set forth in the foregoing, the autonomous driving apparatusaccording to the present invention is configured to receive data fromthe different kinds of sensors, to analyze the received data, andfurther to check the consistency of the data so as to estimate theposition of the ego vehicle, unlike the conventional autonomous drivingapparatus, so that even if any one of the sensors does not work or thereis an error on the data received from any one of the sensors, it ispossible to accurately set the autonomous driving path, thereby ensuringhigh reliability.

According to the present invention, in addition, the effectiveness ofthe lane markers, road markers, curb and traffic sign extracted from thefirst sensor inputs and the effectiveness of the second sensor inputsare checked, and the updating of the first map data and the second mapdata is checked, so that with the use of the checking results, the firstmatching scores and the second matching scores are corrected, therebymore improving the accuracy in the estimation of the position of the egovehicle, and even if the consistency checking result is “NO”, it ispossible to more accurately estimate the position of the ego vehicle.

While the present invention has been described with reference to theparticular illustrative embodiments, it is not to be restricted by theembodiments but only by the appended claims. It is to be appreciatedthat those skilled in the art can change or modify the embodimentswithout departing from the scope and spirit of the present invention.

What is claimed is:
 1. A method for estimating a position of an egovehicle for autonomous driving, the method comprising the steps of:receiving first sensor inputs from first sensors to extract visual roadinformation from the first sensor inputs by an autonomous drivingapparatus; allowing the extracted visual road information to match firstmap data to produce a first matching score group having first matchingscores calculated with respect to position candidates in a positioncandidate group of the ego vehicle by the autonomous driving apparatus;receiving second sensor inputs from second sensors as kinds of sensorsdifferent from the first sensors by the autonomous driving apparatus;allowing the received second sensor inputs to match second map data toproduce a second matching score group having second matching scorescalculated with respect to position candidates in a position candidategroup of the ego vehicle by the autonomous driving apparatus; checkingwhether the first matching score group is consistent to the secondmatching score group by the autonomous driving apparatus; and estimatingany one of the position candidates in the position candidate group ofthe ego vehicle as the position of the ego vehicle according to theconsistency checking result by the autonomous driving apparatus, whereinthe method further comprises: after the step of extracting the visualroad information, checking whether the extracted visual road informationis effective; and checking whether the first map data is updated, andafter the step of receiving the second sensor inputs, checking whetherthe received second sensor inputs are effective; and checking whetherthe second map data is updated, wherein the method further comprises thesteps of: calculating first matching accuracy of the visual roadinformation and the first map data through the effectiveness checkingresult of the visual road information and the updating checking resultof the first map data; and calculating second matching accuracy of thesecond sensor inputs and the second map data through the effectivenesschecking result of the second sensor inputs and the updating checkingresult of the second map data.
 2. The method according to claim 1,wherein the first sensors are camera sensors installed on the egovehicle and the first map data is map data having visual roadinformation such as lane markers, road markers, curb, and traffic sign.3. The method according to claim 1, wherein the second sensors arescanning sensors installed on the ego vehicle and the second map data isthree-dimensional (3D) point cloud map data.
 4. The method according toclaim 1, further comprising the step of applying the first matchingaccuracy and the second matching accuracy as inputs of a weight-basedprobability coupling method to calculate a first weight for the firstmatching accuracy and a second weight for the second matching accuracy.5. The method according to claim 4, wherein the step of estimating anyone of the position candidates as a position of the ego vehiclecomprises the steps of: when the consistency checking result is “NO”,applying the first weight to the first matching scores calculated forthe position candidates in the position candidate group of the egovehicle and applying the second weight to the second matching scorescalculated for the position candidates in the position candidate groupof the ego vehicle to add the applied weights; and estimating theposition candidate having the biggest added result as the position ofthe ego vehicle.
 6. The method according to claim 1, wherein the step ofchecking the consistency comprises the steps of: calculating adifference between a probability distribution of the first matchingscore group and a probability distribution of the second matching scoregroup by means of a probability distribution comparison method; anddetermining whether the calculated difference exceeds empiricalthreshold value.
 7. The method according to claim 1, wherein the step ofestimating any one of the position candidates as a position of the egovehicle comprises the steps of: when the consistency checking result is“YES”, adding the first matching scores and the second matching scorescalculated for the position candidates in the position candidate groupof the ego vehicle; and estimating the position candidate having thebiggest added result as the position of the ego vehicle.
 8. Anautonomous driving apparatus for estimating a position of an egovehicle, comprising: one or more processors; a network interface; amemory for loading a computer program implemented by the processors; anda storage for storing large-capacity network data and the computerprogram, wherein the computer program performs: an operation forreceiving first sensor inputs from first sensors to extract visual roadinformation from the first sensor inputs received; an operation forallowing the extracted visual road information to match first map datato produce a first matching score group having first matching scorescalculated with respect to position candidates in a position candidategroup of the ego vehicle; an operation for receiving second sensorinputs from second sensors as the kinds of sensors different from thefirst sensors; an operation for allowing the received second sensorinputs to match second map data to produce a second matching score grouphaving second matching scores calculated with respect to positioncandidates in a position candidate group of the ego vehicle; anoperation for checking consistency between the first matching scoregroup and the second matching score group; and an operation forestimating any one of the position candidates in the position candidategroup of the ego vehicle as the position of the ego vehicle according tothe consistency checking result, wherein the computer program furtherperforms: after the operation for extracting the visual roadinformation, an operation for checking whether the extracted visual roadinformation is effective; and an operation for checking whether thefirst map data is updated, and after the operation for receiving thesecond sensor inputs, an operation for checking whether the receivedsecond sensor inputs are effective; and an operation for checkingwhether the second map data is updated, wherein the computer programfurther performs: an operation for calculating first matching accuracyof the visual road information and the first map data through theeffectiveness checking result of the visual road information and theupdating checking result of the first map data; and an operation forcalculating second matching accuracy of the second sensor inputs and thesecond map data through the effectiveness checking result of the secondsensor inputs and the updating checking result of the second map data.